code: def _example_serving_receiver_fn(transform_output, schema):
      """Build the serving in inputs.
  
      Args:
        transform_output: a `tft.TFTransformOutput` object.
        schema: the schema of the input data.
  
      Returns:
        Tensorflow graph which parses examples, applying tf-transform to them.
      """
      raw_feature_spec = _get_raw_feature_spec(schema)
      raw_feature_spec.pop(_LABEL_KEY)
  
      raw_input_fn = (
          tf_estimator.export.build_parsing_serving_input_receiver_fn(
              raw_feature_spec, default_batch_size=None
          )
      )
      serving_input_receiver = raw_input_fn()
  
      _, transformed_features = transform_output.transform_raw_features(
          serving_input_receiver.features, drop_unused_features=True
      )
  
      return tf_estimator.export.ServingInputReceiver(
          transformed_features, serving_input_receiver.receiver_tensors
      )
  
file_path: tfx/tfx/examples/custom_components/slack/example/taxi_utils_slack.py
imports: from typing import List
  import tensorflow
  from tensorflow import estimator
  import tensorflow_model_analysis
  import tensorflow_transform
  from tensorflow_transform.tf_metadata import schema_utils
  from tfx.components.trainer.fn_args_utils import DataAccessor
  from tfx_bsl.tfxio import dataset_options
label_desc: Use image node_id 7 for calling a global function with example usage: _example_serving_receiver_fn(transform_output, schema) and returns: tf_estimator